Nonparametric estimation and inference for panel data models

Christopher F. Parmeter, Jeffrey S. Racine

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent than, for example, say, nonparametric density and regression techniques. It is our hope that the material covered in this chapter will prove useful and facilitate its adoption by practitioners.

Original languageEnglish (US)
Title of host publicationPanel Data Econometrics
Subtitle of host publicationTheory
PublisherElsevier
Pages97-129
Number of pages33
ISBN (Electronic)9780128143674
ISBN (Print)9780128144312
DOIs
StatePublished - Jan 1 2019

Keywords

  • Data generating process
  • Generalized least squares
  • Nonparametric estimations
  • One-way error component model
  • Panel data models models
  • Random effects

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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